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A cooperative scheduling framework for shared transportation services 19

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A cooperative scheduling framework for shared transportation...

19th COTA International Conference of Transportation Professionals: Transportation in China - Connecting the World, CICTP 2019

作者： Wang, Xinli Qiao, Yanan Wang, Tianyu Guo, Jifu Zhou, Xuesong Xian, Kai Tong, Lu Carol Xie, Jindong Natl. Eng. Lab. for Compreh. Transp. Big Data Applic. Technol. Sch. of Transp. Sci. and Engineering Beihang Univ. Beijing100191 China State Key Lab of Software Development Environment School of Computer Science and Engineering Beihang Univ. Beijing100191 China Beijing Transport Institute No. 9 LiuLiQiao South Lane Fengtai District China

ISBN: (数字)9780784482292

ISBN: (纸本)9780784482292

To improve the efficiency of metropolitan shared transportation services (STS), this paper proposes a cooperative scheduling framework to avoid congestion. Two procedures are considered: shared path scheduling and congestion solving. In a known static traffic network, classic vehicle routing problem (VRP) solutions may obtain an optimal path. But with an increasing number of shared transportation services required, congestion is likely, triggered by open loop VRP scheduling, especially when shared vehicles overwhelm private ones. Dynamic traffic assignment (DTA) solutions could be employed for short-term traffic situation prediction. However, VRP and DTA cannot be combined linearly, due to uncontrolled recursive calls of routing procedures within VRP and DTA. Hence, a software structure is further studied to guarantee convergence of the cooperative framework. Using two open-source transportation packages, VRPLite and DTALite, we test our framework with the Western 3rd Ring Road of the Beijing regional network. Results verify the framework's usability and flexibility. © ASCE.

关键词： Scheduling

Mining Dual Emotion for Fake News Detection

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arXiv 2019年

作者： Zhang, Xueyao Cao, Juan Li, Xirong Sheng, Qiang Zhong, Lei Shu, Kai Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing China Illinois Institute of Technology ChicagoIL United States

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features;2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.1 2 Copyright © 2019, The Authors. All rights reserved.

关键词： Fake detection

QUANTUM LOVÁSZ LOCAL LEMMA: SHEARER’S BOUND IS TIGHT

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arXiv 2018年

作者： He, Kun Li, Qian Sun, Xiaoming Zhang, Jiapeng The Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China University of Southern California United States

The Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all bad events under some weakly dependent conditions. In a seminal paper, Ambainis, Kempe, and Sattath (JACM 2012) introduced a quantum version LLL (QLLL) which shows the possibility of avoiding all "bad" Hamiltonians under some weakly dependent condition, and applied QLLL to the random k-QSAT problem. Sattath, Morampudi, Laumann, and Moessner (PNAS 2015) extended Ambainis, Kempe, and Sattath’s result and showed that Shearer’s bound is a sufficient condition for QLLL, and conjectured that Shearer’s bound is indeed the tight condition for QLLL. In this paper, we affirm this conjecture. Precisely, we prove that Shearer’s bound is tight for QLLL, i.e., the relative dimension of the smallest satisfying subspace is completely characterized by the independent set polynomial. Our result implies the tightness of Gilyén and Sattath’s algorithm (FOCS 2017), and also implies that the lattice gas partition function fully characterizes quantum satisfiability for almost all Hamiltonians with large enough qudits (Sattath, Morampudi, Laumann and Moessner, PNAS 2015). The commuting LLL (CLLL), which focuses on commuting local Hamiltonians, is also investigated here. We prove that the tight regions of CLLL and QLLL are different in general. This result indicates that it is possible to design an algorithm for CLLL which is still efficient beyond Shearer’s bound. Copyright © 2018, The Authors. All rights reserved.

关键词： Hamiltonians

SAMPLING LOVÁSZ LOCAL LEMMA FOR GENERAL CONSTRAINT SATISFACTION SOLUTIONS IN NEAR-LINEAR TIME

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arXiv 2022年

作者： He, Kun Wang, Chunyang Yin, Yitong The Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China No. 59 Zhongguancun Street Haidian District Beijing China State Key Laboratory for Novel Software Technology Nanjing University 163 Xianlin Avenue Jiangsu Province Nanjing China

We give a fast algorithm for sampling uniform solutions of general constraint satisfaction problems (CSPs) in a local lemma regime. Suppose that the CSP has n variables with domain size at most q, each constraint contains at most k variables, shares variables with at most Δ constraints, and is violated with probability at most p by a uniform random assignment. The algorithm returns an almost uniform satisfying assignment in expected poly(q,k, Δ) ·(n) time, as long as a local lemma condition is satisfied: k · p · q2 · Δ5 ≤ C0 for a suitably small absolute constant C0. Previously, under similar local lemma conditions, sampling algorithms with running time polynomial in both nand Δ were only known for the almost atomic case, where each constraint is violated by a small number of forbidden local configurations. The key term Δ5 in our local lemma condition also improves the previously best known Δ7 for general CSPs [JPV21b] and Δ5.714 for atomic CSPs, including the special case of k-CNF [JPV21a, HSW21]. Our sampling approach departs from previous fast algorithms for sampling LLL, which were based on Markov chains. A crucial step of our algorithm is a recursive marginal sampler that is of independent interests. Within a local lemma regime, this marginal sampler can draw a random value for a variable according to its marginal distribution, at a cost independent of the size of the CSP. Copyright © 2022, The Authors. All rights reserved.

关键词： Constraint satisfaction problems

Creating knowledge and Wisdom via Big data Analytics: Preface for ITQM 2017

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Procedia Computer Science 2017年 122卷 1-9页

作者： Ahuja, Vandana Shi, Yong Khazanchi, Deepak Abidi, Naseem Tian, Yingjie Berg, Daniel Tien, James M. Jaypee Business School A-10 Sector-62 Noida Uttar Pradesh201 307 India School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha Chinese Academy of Sciences OmahaNE68182 United States College of Engineering University of Miami Coral GablesFL33124 United States

EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding

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arXiv 2024年

作者： Huang, Muye Lai, Han Zhang, Xinyu Wu, Wenjun Ma, Jie Zhang, Lingling Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China MOE KLINNS Lab Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs’ capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models’ chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA. © 2024, CC BY.

关键词： Visual languages

An Improved YOLOv3 Network for PCB Defect Detection

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An Improved YOLOv3 Network for PCB Defect Detection

Chinese Automation Congress (CAC)

作者： Guifang Shao Yuyun Zheng Tiejun Li Jianyi Wu Jincai Luo Fengqiang Gao Jianqi Ma Tundong Liu Xiamen Key Lab. of Big Data Intelligent Analysis and Decision Xiamen University Xiamen China School of Aerospace Engineering Xiamen University Xiamen China School of Information Engineering Jimei University Xiamen China

ISBN: (纸本)9781665426480

Different defects with various features and shapes may appeared in one printed circuit board (PCB), making a great challenge for defect detection. To deal with the contradiction between the speed and accuracy of current deep learning networks, we put forward a MobileNet-YOLOv3 network for PCB defect detection. The MobileNet is introduced to replace the backbone network Darknet-53 of YOLOv3, so as to obtain a lightweight network. Meanwhile, the feature pyramid network (FPN) of YOLOv3 is improved by channel reorganization and downsampling to obtain higher accuracy. Experiments on public data set images with 6 types of defects are conducted, comparative results reveal that the proposed MobileNet-YOLOv3 can achieve higher accuracy than YOLOv3 and YOLOv3-Tiny. According to the frames per second factor compared with YOLOv3-Tiny and YOLOv3, the proposed network is faster than YOLOv3.

关键词： Deep learning Automation Shape Printed circuits Human-robot interaction Feature extraction

Elastic-plastic time-history analysis of steel-tube stiff-frame arch bridge without inner filling concrete 3

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Elastic-plastic time-history analysis of steel-tube stiff-fr...

2020 3rd International Symposium on Traffic Transportation and Civil Architecture, ISTTCA 2020

作者： Zhang, Ben Li, Li Su, Wenming School of Civil Engineering Chongqing Jiaotong University Chongqing400074 China China Railway Bridge Science Research Institute Ltd. Wuhan430034 China State Key Laborary for Health and Safety of Bridge Structures Wuhan430034 China Shandong Hi-Speed Engineering Testing Co. Ltd Jinan250000 China Key Lab. for Bridge Struct. Big Data Perf. Diagn. and Treatm. Improvement of Transp. Industry Jinan250000 China

Based on a case study of a typical arch bridge with hollow steel tube and stiffness frame located in the high intensity region of western China, this paper adopts the onlinear time- history method to analyze the structural response of the overall bridge elastic-plastic model under seismic action. Under the force of Wolong wave in Wenchuan Earthquake, the main load-bearing members of the structure have a good seismic performance. Only part of the columns on the main arch ring enter the plastic hinge state for energy dissipation, and the lower columns have a more obvious energy dissipation effect than the higher ones. The result indicates that the design idea of structure which is similar to that of strong column and weak beam in ductility design, has the characteristics of "strong arch and weak beam"and can therefore improve the ability of structure to resist collapse under earthquake action. © Published under licence by IOP Publishing Ltd.

关键词： Energy dissipation

Predicting visual features from text for image and video caption retrieval

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arXiv 2017年

作者： Dong, Jianfeng Li, Xirong Snoek, Cees G.M. College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Lab of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands

This paper strives to find amidst a set of sentences the one best describing the content of a given image or video. Different from existing works, which rely on a joint subspace for their image and video caption retrieval, we propose to do so in a visual space exclusively. Apart from this conceptual novelty, we contribute Word2VisualVec, a deep neural network architecture that learns to predict a visual feature representation from textual input. Example captions are encoded into a textual embedding based on multi-scale sentence vectorization and further transferred into a deep visual feature of choice via a simple multi-layer perceptron. We further generalize Word2VisualVec for video caption retrieval, by predicting from text both 3-D convolutional neural network features as well as a visual-audio representation. Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset and the very recent NIST TrecVid challenge for video caption retrieval detail Word2VisualVec's properties, its benefit over textual embeddings, the potential for multimodal query composition and its state-of-the-art results. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting